Application function consolidation recommendation

ABSTRACT

By analyzing execution of a set of transactions by an application, a set of actual code execution paths of the application are determined. From the set of actual code execution paths, a set of predicted execution paths of the application are predicted using an execution prediction model. The set of predicted execution paths includes the set of actual code execution paths. By determining that paths in the set of predicted execution paths have above a threshold similarity to each other, a cluster of predicted execution paths is identified. The cluster of predicted execution paths is recommended, using a recommendation model, for implementation as a single execution path in a revised version of the application.

BACKGROUND

The present invention relates generally to a method, system, andcomputer program product for software application analysis. Moreparticularly, the present invention relates to a method, system, andcomputer program product for application function consolidationrecommendation.

Many software applications used to process transactions today wereinitially implemented years or decades ago, using the programming andscripting languages (e.g. Cobol, PL/1, or Rexx scripts), and platformscommon when an application was initially designed. These applicationshave typically been added to and updated over time without much analysisof the overall application's architecture. Instead, as applicationdevelopers come and go they often find it easier to add functionality bycopying existing functionality and implementing minor modifications,rather than rewriting existing code.

Application modernization is the process of modernizing an existingsoftware application's platform infrastructure, internal architecture,or features.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product. An embodiment includes a method that determines, byanalyzing execution of a set of transactions by an application, a set ofactual code execution paths of the application. An embodiment predicts,from the set of actual code execution paths using an executionprediction model, a set of predicted execution paths of the application,the set of predicted execution paths including the set of actual codeexecution paths. An embodiment identifies, by determining that paths inthe set of predicted execution paths have above a threshold similarityto each other, a cluster of predicted execution paths. An embodimentrecommends, for implementation as a single execution path in a revisedversion of the application using a recommendation model, the cluster ofpredicted execution paths

An embodiment includes a computer usable program product. The computerusable program product includes one or more computer-readable storagedevices, and program instructions stored on at least one of the one ormore storage devices.

An embodiment includes a computer system. The computer system includesone or more processors, one or more computer-readable memories, and oneor more computer-readable storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example configuration forapplication function consolidation recommendation in accordance with anillustrative embodiment;

FIG. 4 depicts an example of application function consolidationrecommendation in accordance with an illustrative embodiment;

FIG. 5 depicts a continued example of application function consolidationrecommendation in accordance with an illustrative embodiment;

FIG. 6 depicts a flowchart of an example process for applicationfunction consolidation recommendation in accordance with an illustrativeembodiment;

FIG. 7 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 8 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The illustrative embodiments recognize that as a software application isupdated over time, the application often includes inefficiencies.Because application developers often add functionality by copyingexisting functionality and implementing minor modifications, there maybe several portions of code performing very similar functions that couldbe consolidated together, making the overall application smaller andhence more efficient. There may be portions of code that are no longerexecuted at all, but a developer either did not notice or was afraid toremove the code for fear of introducing an error. These unused portionsof code still consume memory and storage space. Additional efficienciesmight also be realized if some portions of, or an entire application,were rewritten in a more modern computer language (e.g. Java) orimplemented using more flexibly configurable interfaces betweencomponents, such as web services or microservices. (Java is a registeredtrademark of Oracle Corporation in the United States and othercountries.)

The illustrative embodiments also recognize that applicationmodernization is difficult without a thorough analysis of an existingapplication to be modernized. However, because applications have beenmodified over many years, with a view towards solving an immediateproblem rather than preserving an efficient architecture, identifyingportions of code to be modernized is difficult for human developers.Because application developers often add functionality by copyingexisting functionality and implementing minor modifications, manyfunctions look similar but perform slightly differently. Code might havebeen updated without updating documentation of that code, or there mightbe no documentation at all. Application code might be specific to aparticular platform, architecture, or obsolete hardware without thisspecificity being obvious to a developer. In addition, the greatestefficiency improvements are obtained when software code that is usedmost frequently is modernized, and when as many functions as possibleare consolidated together. Although tools are presently available todetermine which portions of an application are used most frequently,such tools are not well integrated with the additional neededfunctionality. As well, human developers find it difficult to determinewhich functions are similar to which other functions, and measuresimilarity accurately. Consequently, the illustrative embodimentsrecognize that there is an unmet need for an automated system thatanalyzes both source code and performance of an existing application tobe modernized, while the application is executing, and identifies andrecommends specific application functions for consolidation.

The illustrative embodiments recognize that the presently availabletools or solutions do not address these needs or provide adequatesolutions for these needs. The illustrative embodiments used to describethe invention generally address and solve the above-described problemsand other problems related to application function consolidationrecommendation.

An embodiment can be implemented as a software application. Theapplication implementing an embodiment can be configured as amodification of an existing application development system, as aseparate application that operates in conjunction with an existingapplication development system, a standalone application, or somecombination thereof.

Particularly, some illustrative embodiments provide a method thatdetermines, by analyzing execution of a set of transactions by anapplication, a set of actual code execution paths of the application,uses an execution prediction model to predict a set of predictedexecution paths of the application, identifies one or more clusters ofsimilar predicted execution paths, and recommends a cluster of predictedexecution paths for implementation as a single execution path in arevised version of the application.

An embodiment analyzes changes in application code over time. Developerstypically modify some portions of an application's source code morefrequently than other portions. Because rarely-modified applicationportions have had less developer attention than frequently-modifiedportions, the rarely-modified portions are less likely to already be upto date and should benefit most from an application modernizationprocess. Thus, an embodiment uses presently available techniques toanalyze a repository of application source code to determine whichportions of source code are checked into the repository and how oftencheck-ins occur. An embodiment also uses presently available techniquesto analyze deployments of portions of, or an entire application, aplatform or other infrastructure on which a deployment executes, and howoften deployments occur. An embodiment uses code repository anddeployment data to determine an application's initial deployment,frequency of source code updates and deployments, which portions areupdated and deployed most and least frequently, and an application'ssource code and executable code at a particular time.

An embodiment analyzes an application's execution of a set oftransactions to determine a set of actual code execution paths of theapplication. Applications process transactions. For example, in anapplication that processes credit card payments, the set of transactionsmight include operations to validate each of a credit card number,expiration date, and security code, an operation to determine that aparticular purchase amount does not exceed the cardholder's creditlimit, and operations to debit the cardholder's account and credit thepayee's account appropriately. In one embodiment, the set oftransactions is the normal operation of the application being analyzed,either continuously or for a predetermined period of time. In anotherembodiment, the set of transactions includes transactions intended toexercise particular execution paths of an application that might not beexecuted or might rarely be executed in the application's everydayoperations. For example, a credit card processing application mightinclude a set of reconciliation functions that are normally executed atmonth-end or year-end, outside the period during which the applicationis being analyzed, and thus an additional set of transactions using thereconciliation functions might be generated for execution analysis. Asanother example, the set of transactions might include a set of testtransactions, such as a test suite intended to validate correctimplementation of a new application deployment. Presently availabletechniques for application analysis include real-time applicationexecution monitoring and code parsing monitoring, transaction log fileanalysis, and historical usage documentation analysis. Other applicationexecution analysis techniques are also available and contemplated withinthe scope of the illustrative embodiments.

One embodiment formats application execution analysis results into acomputation graph. A computation graph (also called a computationalgraph) is a directed graph in which nodes of the graph representoperations on data and edges of the graph represent inputs (i.e.function parameters) to nodes. Thus, the embodiment produces acomputation graph representing actual code execution paths of theapplication being analyzed. In the computation graph, a projection is anexecution path of the application. Some nodes join multiple projectionsas a union (a set containing all the elements of all input sets) andsome nodes join multiple projections as an intersection (a setcontaining elements common to all input sets).

An embodiment uses a set of actual code execution paths of anapplication being analyzed to predict a set of possible code executionpaths of the application. When application execution is being monitored,the transactions the application processes might not execute all of theapplication's execution paths. Thus, an embodiment supplements the setof actual code execution paths with a set of possible code executionpaths.

One embodiment uses an execution prediction model implemented as aneural network model to predict a set of possible code execution paths.In particular, the embodiment computes a permutation of an actual codeexecution path and uses the neural network model to predict aprobability that the application will execute the permutation inresponse to a transaction that was not received during executionmonitoring. One embodiment also predicts a confidence level in thepredicted probability. One embodiment uses, as the predictedprobability, a weighted sum of probabilities of previous permutations,where each weight represents a confidence level of a correspondingprobability. If each probability is a range between −1.0 (representingzero percent probability) and +1.0 (representing one hundred percentprobability), and ff the weighted sum is negative, this indicates thatthe permutation cannot happen, and the permutation's probability is setto zero. If each probability is a range between zero (representing zeropercent probability) and one (representing one hundred percentprobability), and if the weighted sum is zero, this indicates that thepermutation cannot happen, and the permutation's probability is set tozero. An embodiment incorporates the set of possible execution pathsinto a computation graph representing actual code execution paths of theapplication being analyzed. The resulting computation graph includesboth actual and possible execution paths of the application.

An embodiment continues to monitor application execution, compares theapplication's actual execution paths with predicted execution paths, anduses the comparison results to train the neural network model. Trainingrewards predictions that actually occur and penalizes predictions thatdo not occur, thus refining the model over time.

An embodiment determines that a set of paths in the set of predicted andactual execution paths have above a threshold similarity to each other,and forms the set of paths into a cluster of predicted execution paths.One embodiment uses the computation graph to cluster paths using avariant of a presently available mean-shifting algorithm. The algorithmidentifies paths in the predicted computation graph with high densityusing multiple mean-shifting steps to generate multiple local densitymaximums, thus finding multiple clusters.

An embodiment uses a recommendation model to recommend a cluster ofpredicted execution paths for implementation as a single execution pathin a revised version of the application being analyzed. Therecommendation model takes into account one or more recommendationfactors. One recommendation factor is the impact consolidating a clusterwould have on interactions between the functions being consolidated andother application functionality. Because incorrectly managing suchinteractions can lead to application bugs, a cluster with fewerinteractions is preferred over a cluster with more interactions. Anotherrecommendation factor is the number of functions that can beconsolidated together or the size of the cluster. Consolidating a largenumber of functions or a large cluster together produces greaterapplication efficiencies and space savings than consolidating fewerfunctions or a smaller cluster, at the cost of a similar amount ofdeveloper time; thus consolidating more functions or a larger cluster ispreferred over consolidating fewer functions or a smaller cluster.Another recommendation factor is the maturity and expected future usageof the functionality or cluster to be consolidated, because if the codeis still under active development or is not expected to be needed in thefuture there is little to be gained from a consolidation. Anotherrecommendation factor is a cost savings resulting from a consolidation,including application maintenance costs (measured in developers' time)and the compute efficiency of the revised application. Otherrecommendation factors are also possible and contemplated within thescope of the illustrative embodiments.

Once a user accepts and implements a recommendation, an embodimentrepeats the application analysis process to determine a set of actualexecution paths of the revised application and compares the revisedapplication's execution paths with those of the original application.The embodiment uses the comparison to adjust the recommendation model.For example, if a cluster of execution paths was consolidated but didnot result in as great an efficiency improvement as projected, anembodiment reduces a projected efficiency improvement for a similarcluster.

The manner of application function consolidation recommendationdescribed herein is unavailable in the presently available methods inthe technological field of endeavor pertaining to software applicationdevelopment. A method of an embodiment described herein, whenimplemented to execute on a device or data processing system, comprisessubstantial advancement of the functionality of that device or dataprocessing system in determining, by analyzing execution of a set oftransactions by an application, a set of actual code execution paths ofthe application, using an execution prediction model to predict a set ofpredicted execution paths of the application, identifying one or moreclusters of similar predicted execution paths, and recommending acluster of predicted execution paths for implementation as a singleexecution path in a revised version of the application.

The illustrative embodiments are described with respect to certain typesof application analyses, permutations, predictions, computation graphs,clusters, recommendation factors, thresholds, adjustments, devices, dataprocessing systems, environments, components, and applications only asexamples. Any specific manifestations of these and other similarartifacts are not intended to be limiting to the invention. Any suitablemanifestation of these and other similar artifacts can be selectedwithin the scope of the illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas example and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner.

Application 105 implements an embodiment described herein. Application105 executes in any of servers 104 and 106, clients 110, 112, and 114,and device 132.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114,and device 132 may couple to network 102 using wired connections,wireless communication protocols, or other suitable data connectivity.Clients 110, 112, and 114 may be, for example, personal computers ornetwork computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Data processing environment 100 may also take the form of a cloud, andemploy a cloud computing model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g. networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object oriented or other type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on data processing system 200.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 in FIG. 1,are located on storage devices, such as in the form of code 226A on harddisk drive 226, and may be loaded into at least one of one or morememories, such as main memory 208, for execution by processing unit 206.The processes of the illustrative embodiments may be performed byprocessing unit 206 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 208, read onlymemory 224, or in one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. in another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of dataprocessing system 200 using virtualized manifestation of some or allcomponents depicted in data processing system 200. For example, in avirtual machine, virtual device, or virtual component, processing unit206 is manifested as a virtualized instance of all or some number ofhardware processing units 206 available in a host data processingsystem, main memory 208 is manifested as a virtualized instance of allor some portion of main memory 208 that may be available in the hostdata processing system, and disk 226 is manifested as a virtualizedinstance of all or some portion of disk 226 that may be available in thehost data processing system. The host data processing system in suchcases is represented by data processing system 200.

With reference to FIG. 3, this figure depicts a block diagram of anexample configuration for application function consolidationrecommendation in accordance with an illustrative embodiment.Application 300 is an example of application 105 in FIG. 1 and executesin any of servers 104 and 106, clients 110, 112, and 114, and device 132in FIG. 1.

Analysis module 310 analyzes changes in application code over time.Using presently available techniques, module 310 analyzes a repositoryof application source code to determine which portions of source codeare checked into the repository and how often check-ins occur. Module310 also uses presently available techniques to analyze deployments ofportions of, or an entire application, a platform or otherinfrastructure on which a deployment executes, and how often deploymentsoccur. Module 310 uses code repository and deployment data to determinean application's initial deployment, frequency of source code updatesand deployments, which portions are updated and deployed most and leastfrequently, and an application's source code and executable code at aparticular time.

Analysis module 310 analyzes an application's execution of a set oftransactions to determine a set of actual code execution paths of theapplication. In one implementation of module 310, the set oftransactions is the normal operation of the application being analyzed,either continuously or for a predetermined period of time. In anotherimplementation of module 310, the set of transactions includestransactions intended to exercise particular execution paths of anapplication that might not be executed or might rarely be executed inthe application's everyday operations. Presently available techniquesfor application analysis include real-time application executionmonitoring and code parsing monitoring, transaction log file analysis,and historical usage documentation analysis. Module 310 formatsapplication execution analysis results into a computation graph.

Execution prediction module 320 uses an execution prediction modelimplemented as a neural network model to predict a set of possible codeexecution paths of the application from the set of actual code executionpaths of the application. In particular, module 320 computes apermutation of an actual code execution path and uses the neural networkmodel to predict a probability that the application will execute thepermutation in response to a transaction that was not received duringexecution monitoring. One implementation of module 320 uses, as thepredicted probability, a weighted sum of probabilities of previouspermutations, where each weight represents a confidence level of acorresponding probability. Module 320 also predicts a confidence levelin the predicted probability. Module 320 incorporates the set ofpossible execution paths into a computation graph already representingactual code execution paths of the application being analyzed. Theresulting combined computation graph includes both actual and possibleexecution paths of the application.

Module 310 continues to monitor application execution. Module 320compares the application's actual execution paths with predictedexecution paths and uses the comparison results to train the neuralnetwork model. Training rewards predictions that actually occur andpenalizes predictions that do not occur, thus refining the model overtime.

Clustering module 330 determines that a set of paths in the set ofpredicted and actual execution paths have above a threshold similarityto each other, and forms the set of paths into a cluster of predictedexecution paths. One implementation of module 330 uses the computationgraph to cluster paths using a variant of a mean-shifting algorithm.

Recommendation module 340 uses a recommendation model to recommend acluster of predicted execution paths for implementation as a singleexecution path in a revised version of the application being analyzed.The recommendation model takes into account one or more recommendationfactors. One recommendation factor is the impact consolidating a clusterwould have on interactions between the functions being consolidated andother application functionality. Because incorrectly managing suchinteractions can lead to application bugs, a cluster with fewerinteractions is preferred over a cluster with more interactions. Anotherrecommendation factor is the number of functions that can beconsolidated together or the size of the cluster. Consolidating a largenumber of functions or a large cluster together produces greaterapplication efficiencies and space savings than consolidating fewerfunctions or a smaller cluster, at the cost of a similar amount ofdeveloper time; thus consolidating more functions or a larger cluster ispreferred over consolidating fewer functions or a smaller cluster.Another recommendation factor is the maturity and expected future usageof the functionality or cluster to be consolidated, because if the codeis still under active development or is not expected to be needed in thefuture there is little to be gained from a consolidation. Anotherrecommendation factor is a cost savings resulting from a consolidation,including application maintenance costs (measured in developers' time)and the compute efficiency of the revised application. Otherrecommendation factors are also possible.

Once a user accepts and implements a recommendation, module 310 repeatsthe application analysis process to determine a set of actual executionpaths of the revised application and compares the revised application'sexecution paths with those of the original application. Module 340 usesthe comparison to adjust the recommendation model. For example, if acluster of execution paths was consolidated but did not result in asgreat an efficiency improvement as projected, an embodiment reduces aprojected efficiency improvement for a similar cluster.

With reference to FIG. 4, this figure depicts an example of applicationfunction consolidation recommendation in accordance with an illustrativeembodiment. The example can be executed using application 300 in FIG. 3.

Here, analysis module 310 has analyzed an application being executed,generating actual execution paths 410, a segment of a computationalgraph. Execution prediction module 320 uses a neural network model topredict a set of possible code execution paths of the application, andincorporates the set of possible execution paths into predictedexecution paths 420, a computation graph including both actual andpossible execution paths of the application being analyzed.

With reference to FIG. 5, this figure depicts a continued example ofapplication function consolidation recommendation in accordance with anillustrative embodiment. Predicted execution paths 420 is the same aspredicted execution paths 420 in FIG. 4.

Here, clustering module 330 has analyzed predicted execution paths 420and determined that the paths in cluster 510 have above a thresholdsimilarity to each other and determined that the paths in cluster 520have above a threshold similarity to each other. Thus, recommendationmodule 340 produces recommendation 530, recommending that thefunctionality of cluster 510 be combined into one new function and thatthe functionality of cluster 520 be combined into another new function.

With reference to FIG. 6, this figure depicts a flowchart of an exampleprocess for application function consolidation recommendation inaccordance with an illustrative embodiment. Process 600 can beimplemented in application 300 in FIG. 3.

In block 602, the application analyzes execution of a set oftransactions by an application to determine a set of actual codeexecution paths of the application. In block 604, the application usesan execution prediction model and the set of actual code execution pathsto predict a set of predicted execution paths of the application. Inblock 606, the application identifies a cluster of paths by determiningthat paths in the predicted and actual execution paths have above athreshold similarity to each other. In block 608, the applicationrecommends the cluster of paths for implementation as a single executionpath in a revised version of the application. Then the application ends.

Referring now to FIG. 7, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-Ndepicted are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functionsdepicted are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and application selection based on cumulativevulnerability risk assessment 96.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forapplication function consolidation recommendation and other relatedfeatures, functions, or operations. Where an embodiment or a portionthereof is described with respect to a type of device, the computerimplemented method, system or apparatus, the computer program product,or a portion thereof, are adapted or configured for use with a suitableand comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

What is claimed is:
 1. A computer-implemented method comprising:predicting, from a set of actual code execution paths using an executionprediction model, a set of predicted execution paths of an application,the set of predicted execution paths including the set of actual codeexecution paths; identifying, by determining that paths in the set ofpredicted execution paths have above a threshold similarity to eachother, a cluster of predicted execution paths; and recommending, forimplementation as a single execution path in a revised version of theapplication using a recommendation model, the cluster of predictedexecution paths.
 2. The computer-implemented method of claim 1, whereinthe predicting further comprises: computing a permutation of an actualcode execution path in the set of actual code execution paths;predicting, for the permutation, a probability; and adding, responsiveto the probability being above a threshold probability, the permutationto the set of predicted execution paths.
 3. The computer-implementedmethod of claim 2, wherein predicting the probability further comprisespredicting a confidence level corresponding to the probability.
 4. Thecomputer-implemented method of claim 1, wherein the recommending isbased on a number of interactions between the cluster and a portion ofthe application outside the cluster.
 5. The computer-implemented methodof claim 1, wherein the recommending is based on a size of the cluster.6. The computer-implemented method of claim 1, wherein the recommendingis based on an expected future usage of the cluster.
 7. Thecomputer-implemented method of claim 1, wherein the recommending isbased on a predicted cost savings of executing the revised version ofthe application relative to the application.
 8. The computer-implementedmethod of claim 1, wherein the set of actual code execution paths of theapplication is determined by analyzing execution of a set oftransactions by the application, further comprising: determining, byanalyzing execution of a second set of transactions by the application,a second set of actual code execution paths of the application; andadjusting, based on a comparison between the second set of actual codeexecution paths and the set of predicted execution paths, the executionprediction model.
 9. The computer-implemented method of claim 1, furthercomprising: determining, by analyzing execution of a set of transactionsby the revised version of the application, a different set of actualcode execution paths of the revised version of the application; andadjusting, based on a comparison between the different set of actualcode execution paths and the set of predicted execution paths, therecommendation model.
 10. A computer program product for applicationfunction consolidation recommendation, the computer program productcomprising: one or more computer readable storage media, and programinstructions collectively stored on the one or more computer readablestorage media, the program instructions comprising: program instructionsto predict, from a set of actual code execution paths using an executionprediction model, a set of predicted execution paths of an application,the set of predicted execution paths including the set of actual codeexecution paths; program instructions to identify, by determining thatpaths in the set of predicted execution paths have above a thresholdsimilarity to each other, a cluster of predicted execution paths; andprogram instructions to recommend, for implementation as a singleexecution path in a revised version of the application using arecommendation model, the cluster of predicted execution paths.
 11. Thecomputer program product of claim 10, wherein the program instructionsto predict further comprises: program instructions to compute apermutation of an actual code execution path in the set of actual codeexecution paths; program instructions to predict, for the permutation, aprobability; and program instructions to add, responsive to theprobability being above a threshold probability, the permutation to theset of predicted execution paths.
 12. The computer program product ofclaim 11, wherein program instructions to predict the probabilityfurther comprises program instructions to predict a confidence levelcorresponding to the probability.
 13. The computer program product ofclaim 10, wherein the recommending is based on a number of interactionsbetween the cluster and a portion of the application outside thecluster.
 14. The computer program product of claim 10, wherein therecommending is based on a size of the cluster.
 15. The computer programproduct of claim 10, wherein the recommending is based on an expectedfuture usage of the cluster.
 16. The computer program product of claim10, wherein the recommending is based on a predicted cost savings ofexecuting the revised version of the application relative to theapplication.
 17. The computer program product of claim 10, wherein thestored program instructions are stored in the at least one of the one ormore storage media of a local data processing system, and wherein thestored program instructions are transferred over a network from a remotedata processing system.
 18. The computer program product of claim 10,wherein the stored program instructions are stored in the at least oneof the one or more storage media of a server data processing system, andwherein the stored program instructions are downloaded over a network toa remote data processing system for use in a computer readable storagedevice associated with the remote data processing system.
 19. Thecomputer program product of claim 10, wherein the computer programproduct is provided as a service in a cloud environment.
 20. A computersystem comprising one or more processors, one or more computer-readablememories, and one or more computer-readable storage devices, and programinstructions stored on at least one of the one or more storage devicesfor execution by at least one of the one or more processors via at leastone of the one or more memories, the stored program instructionscomprising: program instructions to predict, from a set of actual codeexecution paths using an execution prediction model, a set of predictedexecution paths of an application, the set of predicted execution pathsincluding the set of actual code execution paths; program instructionsto identify, by determining that paths in the set of predicted executionpaths have above a threshold similarity to each other, a cluster ofpredicted execution paths; and program instructions to recommend, forimplementation as a single execution path in a revised version of theapplication using a recommendation model, the cluster of predictedexecution paths.